MultiXNet: Multiclass Multistage Multimodal Motion Prediction
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One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future. To address this task we propose MultiXNet, an end-to-end approach for detection and motion prediction based directly on lidar sensor data. This approach builds on prior work by handling multiple classes of traffic actors, adding a jointly trained second-stage trajectory refinement step, and producing a multimodal probability distribution over future actor motion that includes both multiple discrete traffic behaviors and calibrated continuous position uncertainties. The method was evaluated on large-scale, real-world data collected by a fleet of SDV s in several cities, with the results indicating that it outperforms existing state-of-the-art approaches.
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Only a few multimodal-based multi-stage detection frameworks have been developed and have achieved outstanding detection performance on benchmark datasets.
“More recently, only a few multimodal-based multi-stage detection frameworks [191–193] have been developed and have achieved outstanding detection performance on benchmark datasets.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [191]
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Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction ModelsPreprint
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Deep learning for radar data exploitation of autonomous vehicle
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Self-citationAligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow
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Weakly Supervised Bird's Eye View Motion Prediction With Object Prior
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- Authors
- Djuric, N., Cui, H., Su, Z., Wu, S., Wang, H., Chou, F.-C., Martin, L. S., Feng, S., Hu, R., Xu, Y., Dayan, A., Zhang, S., Becker, B. C., Meyer, G. P., Vallespi-Gonzalez, C., & Wellington, C. K.
- Affiliations
- Uber Advanced Technologies Group
- Venue
- 2021 IEEE Intelligent Vehicles Symposium (IV)
- Publisher
- IEEE
- Published
- 2021-07-11
- Pages
- 435-442
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W3033207664
- Cited by
- 53
- References
- 46
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 7 · 2023: 7 · 2022: 12 · 2021: 17 · 2020: 3
- FWCI
- 10.65
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Intelligent Vehicles Symposium (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Keywords
- motion prediction, autonomous vehicles, LiDAR, trajectory refinement, multimodal distributions, uncertainty calibration, traffic behavior, end-to-end learning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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